The ability to map the extent of wooded vegetation cover over large areas using remote sensing is important for managing and assessing rangelands. Currently, applied techniques are inadequate because they 1) do not directly measure the amount of land covered by woody plants and rely on low-resolution images, 2) require considerable training-area data to train a classifier, and 3) describe only a limited number of land cover types. This paper presents an innovative methodology for creating a land-cover map that requires little to no traditional, training-area data collection before classification. The procedure combines both high-resolution aerial photography (resampled to 2.5-m pixels) and lower-resolution satellite imagery (30-m pixels) to produce a detailed and easily producible data set. The resulting data set also categorizes regions into a wide variety of land cover types in addition to differing levels of wooded cover. This new methodology was applied to the Upper Guadalupe River watershed in Texas, which is composed of varying amounts of brush cover between herbaceous range and dense cover. Validation by comparison to aerial imagery demonstrated a 74.4% success rate for all land cover classes. Validation was also performed by ground survey for several brush-covered points and showed a 90.0% success rate. As a result of the ground survey, modifications to the methodology were recommended to reduce classification errors and improve the process.